As consumer interaction with online resources (e.g., use of web resources, e-commerce, browsing activity, etc.) has grown digital marketing too has becoming increasingly more common. Generally, digital marketers seek to deliver offers for products, services, and content to consumers who will find the offers favorable and have a high probability of responding to the offers. Accordingly, one challenge faced by digital marketers is matching of offers to users so as to maximize the likelihood that users will accept the offers and accordingly optimize the return/reward to the digital marketers derived from the offers.
Traditionally, marketing schemes and corresponding offer selections are largely myopic in nature in that the traditional models primarily consider current conditions and short-term objectives. For instance, such schemes make determinations by considering just the next action/offer state in isolation from other past and/or future action/offer states. By neglecting to consider inter-state dependencies of actions and long-term objectives, traditional models may not adequately achieve maximization of long-term objectives, such as for revenue, satisfaction, offer acceptance, and so forth.